我正在使用Keras功能API,并且对训练批次中上一层的平均输出感兴趣。
我尝试仅在Dense层的输出上调用Keras Average层。
这是一个简单的例子。
from keras.models import Model
from keras import layers
from keras import Input
from keras.utils import plot_model
input_tensor = layers.Input(shape=(784,))
output = layers.Dense(10,)(input_tensor)
average = layers.Average()(output)
avgout = Model(input_tensor, avgout)
avgout.summary()
我想要的是“ avgout”层,请给我输出层的平均输出。结果:
ValueError Traceback (most recent call last)
<ipython-input-7-9d5576113651> in <module>
6 input_tensor = layers.Input(shape=(784,))
7 output = layers.Dense(10,)(input_tensor)
----> 8 average = layers.Average()(output)
9 avgout = Model(input_tensor, avgout)
10 avgout.summary()
~/anaconda3/lib/python3.7/site-packages/keras/engine/base_layer.py in __call__(self, inputs, **kwargs)
429 'You can build it manually via: '
430 '`layer.build(batch_input_shape)`')
--> 431 self.build(unpack_singleton(input_shapes))
432 self.built = True
433
~/anaconda3/lib/python3.7/site-packages/keras/layers/merge.py in build(self, input_shape)
66 # Used purely for shape validation.
67 if not isinstance(input_shape, list):
---> 68 raise ValueError('A merge layer should be called '
69 'on a list of inputs.')
70 if len(input_shape) < 2:
ValueError: A merge layer should be called on a list of inputs.
答案 0 :(得分:0)
Keras计算机中的“平均”层是多个张量的平均值,而不是一个张量的平均值。
您可以使用keras后端的意思是:
from keras import backend as K
from keras.models import Model
from keras import layers
from keras import Input
from keras.utils import plot_model
def mean(input):
return K.mean(input, axis=1)
input_tensor = layers.Input(shape=(784,))
output = layers.Dense(10,)(input_tensor)
average = layers.Lambda(mean, input_shape=(10,))(output)
avgout = Model(input_tensor, average)
avgout.summary()